The research and research projects at the Statistical Learning Laboratory (SaLLy) include both methodology and applications. We are particularly interested in high-dimensional modeling, time series analysis and forecasting, spatio-temporal modeling, sports analytics, quantitative and statistical genetics, machine learning, neural networks, artificial intelligence, and general statistical learning methodologies with application to many disciplines, including economics, finance, environment, energy, public policy, business, and industry.
SaLLy is open to establishing collaborations with other research groups locally, nationally, and internationally. Research collaboration includes data analysis support related to master's or PhD thesis and research projects, in all disciplines. Contact us at SaLLy.Laboratory@gmail.com for more information.
Creation of machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.
Read moreExtremely large and complex data sets that require advanced computational and analytical methods to process and extract insights.
Read moreThe use of statistical and mathematical techniques to enable machines to learn from data and improve their ability to perform specific tasks.
Read moreThe application of statistical and machine learning techniques to sports data to gain insights into player performance, team strategies, and game outcomes.
Read moreThe application of statistical and machine learning techniques to analyze time-stamped data and make predictions about future values of a variable.
Read moreThe development of mathematical models and algorithms to analyze and predict patterns in data that vary in both space and time.
Read moreThe use of statistical methods to analyze genetic data and gain insights into the genetic basis of complex traits and diseases.
Read moreOpen calls for postdoctoral studies at SaLLy. The activities can be conducted locally at the Federal University of Bahia or remotely.
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